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Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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A Method to Quantify Visual Information Processing in Children Using Eye Tracking

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Published on: July 9, 2016

Computer Vision for Brain Disorders Based Primarily on Ocular Responses.

Xiaotao Li^1,2,3,4,5, Fangfang Fan⁶, Xuejing Chen⁷

¹Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Frontiers in Neurology

|May 10, 2021

Summary

Ocular responses like saccades and pupillary changes can serve as early biomarkers for brain disorders. Artificial intelligence, particularly computer vision, offers novel ways to evaluate these features for diagnosing conditions like Alzheimer's disease.

Keywords:

brain disorders cognitive neuroscience computer vision eye-brain engineering ocular assessment retina

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Last Updated: Nov 6, 2025

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Area of Science:

Neuroscience and Ophthalmology
Cognitive and Psychiatric Disorders
Artificial Intelligence in Healthcare

Background:

Ocular responses are intrinsically linked to central nervous system functions, including emotional and cognitive processing.
Specific eye movement patterns, pupillary dynamics, and blinking behaviors are associated with neurological and psychiatric conditions.
Ophthalmic imaging reveals retinal microvasculature and morphology, offering potential insights into brain health.

Purpose of the Study:

To review non-invasive ocular assessment techniques for early detection of brain disorders.
To explore the utility of ocular biomarkers in identifying conditions such as autism spectrum disorder (ASD), Alzheimer's disease (AD), schizophrenia (SZ), and major depressive disorder (MDD).
To highlight the emerging role of artificial intelligence (AI) in analyzing ocular data for cognitive neuroscience applications.

Main Methods:

Review of existing literature on ocular response patterns and their correlation with brain disorders.
Examination of various ophthalmic imaging techniques for visualizing ocular structures and microvasculature.
Exploration of machine learning, specifically computer vision and deep learning, for analyzing ocular features.

Main Results:

Ocular assessments, including saccades, pupillary responses, and spontaneous blinking, show promise as potential biomarkers.
Office-based ophthalmic imaging can visualize relevant retinal characteristics.
AI, particularly computer vision, demonstrates significant potential for novel evaluations and interventions in cognitive neuroscience.

Conclusions:

Ocular metrics offer a non-invasive avenue for the early screening and evaluation of cognitive and psychiatric disorders.
AI-powered analysis of ocular features represents a promising frontier for diagnosing and understanding brain disorders.
Integrating AI with ocular assessments could lead to significant advancements in cognitive neuroscience and clinical practice.